arXiv:2504.19375v2 Announce Type: replace-cross Abstract: Two-time-scale stochastic approximation (SA) is an algorithm with coupled iterations which has found broad applications in reinforcement learning, optimization and game control. In this work, we derive mean squared error bounds for non-linear two-time-scale iterations with contractive mappings. In the setting where both stepsizes are order $Theta(1/k)$, commonly referred to as single time-scale SA with multiple coupled sequences, we obtain the first $O(1/k)$ rate without imposing additional smoothness assumptions. In the setting with […]

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Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose Meta-Experience Learning […]

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Machine learning (ML) offers transformative potential for computational fluid dynamics (CFD), promising to accelerate simulations, improve turbulence modelling, and enable real-time flow prediction and control-capabilities that could fundamentally change how engineers approach fluid dynamics problems. However, the exploration of ML in fluid dynamics is critically hampered by the scarcity of large, diverse, and high-fidelity datasets suitable for training robust models. This limitation is particularly acute for highly turbulent flows, which dominate practical engineering applications yet remain computationally prohibitive […]

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The primary claim I make in Why Authorization is the Hard Problem in Agentic AI is that static authorization models are insufficient for systems that plan, act, and replan over time. In agentic systems, authorization cannot be a one-time gate checked before execution begins. It must be evaluated as part of the agent’s control loop. In this post, I’ll walk through a concrete demo that shows what this looks like in practice. Using OpenClaw and Cedar, we modify the agent […]

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arXiv:2601.02562v1 Announce Type: new Abstract: The rapid growth of dermatological imaging and mobile diagnostic tools calls for systems that not only demonstrate empirical performance but also provide strong theoretical guarantees. Deep learning models have shown high predictive accuracy; however, they are often criticized for lacking well, calibrated uncertainty estimates without which these models are hardly deployable in a clinical setting. To this end, we present the Conformal Bayesian Dermatological Classifier (CBDC), a well, founded framework that combines Statistical […]

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arXiv:2602.16756v1 Announce Type: new Abstract: We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access security, NESSiE reveals safety-relevant failures that should not exist, given the low complexity of the tasks. NESSiE is intended as a lightweight, easy-to-use sanity check for language model safety and, as such, is not sufficient for guaranteeing safety in general — but we argue that passing this test is necessary for any deployment. […]

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Hi everyone, in the past few months, a few of my friends and I have developed this library containing implementation of several popular Linear RNNs, with accelerated kernels for inference and training (similar to mamba). All in PyTorch. The code is fully open source and under an MIT license. The repository also contains the technical report (which was accepted to EACL SRW 2026). Feedback / contributions welcome! https://github.com/SforAiDl/lrnnx submitted by /u/simple-Flat0263 [link] [comments]

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arXiv:2601.19925v1 Announce Type: new Abstract: Introduction: Large language models (LLMs) can process requests and generate texts, but their feasibility for assessing complex academic content needs further investigation. To explore LLM’s potential in assisting scientific review, this study examined ChatGPT-5, Gemini-3-Pro, and Claude-Sonnet-4.5’s consistency and reliability in evaluating abstracts compared to one another and to human reviewers. Methods: 160 abstracts from a local conference were graded by human reviewers and three LLMs using one rubric. Composite score distributions across […]

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